首页|Erasmus University Medical Center Reports Findings in Carpal Tunnel Syndrome (Algorithm Versus Expert: Machine Learning Versus Surgeon-Predicted Symptom Improvement After Carpal Tunnel Release)
Erasmus University Medical Center Reports Findings in Carpal Tunnel Syndrome (Algorithm Versus Expert: Machine Learning Versus Surgeon-Predicted Symptom Improvement After Carpal Tunnel Release)
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New research on Nervous System Diseases and Conditions - Carpal Tunnel Syndrome is the subject of a report. According to news reporting originating from Rotterdam, Netherlands, by NewsRx correspondents, research stated, “Surgeons rely on clinical experience when making predictions about treatment effects. Incorporating algorithm-based predictions of symptom improvement after carpal tunnel release (CTR) could support medical decision-making.” Our news editors obtained a quote from the research from Erasmus University Medical Center, “However, these algorithm-based predictions need to outperform predictions made by surgeons to add value. We compared predictions of a validated prediction model for symptom improvement after CTR with predictions made by surgeons. This cohort study included 97 patients scheduled for CTR. Preoperatively, surgeons estimated each patient's probability of improvement 6 months after surgery, defined as reaching the minimally clinically important difference on the Boston Carpal Tunnel Syndrome Symptom Severity Score. We assessed model and surgeon performance using calibration (calibration belts), discrimination (area under the curve [AUC]), sensitivity, and specificity. In addition, we assessed the net benefit of decision-making based on the prediction model's estimates vs the surgeon's judgement. The surgeon predictions had poor calibration and suboptimal discrimination (AUC 0.62, 95%-CI 0.49-0.74), while the prediction model showed good calibration and appropriate discrimination (AUC 0.77, 95%-CI 0.66-0.89, P = .05). The accuracy of surgeon predictions was 0.65 (95%-CI 0.37-0.78) vs 0.78 (95%-CI 0.67-0.89) for the prediction model (P = .03). The sensitivity of surgeon predictions and the prediction model was 0.72 (95%-CI 0.15-0.96) and 0.85 (95%-CI 0.62-0.97), respectively (P = .04). The specificity of the surgeon predictions was similar to the model's specificity (P = .25). The net benefit analysis showed better decision-making based on the prediction model compared with the surgeons' decision-making (ie, more correctly predicted improvements and/or fewer incorrectly predicted improvements). The prediction model outperformed surgeon predictions of improvement after CTR in terms of calibration, accuracy, and sensitivity.”
RotterdamNetherlandsEuropeAlgorithmsCarpal Tunnel SyndromeCyborgsEmerging TechnologiesMachine LearningNervous System Diseases and ConditionsNeurology
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